BLOG — July 09, 2025
Credit Memo Automation: Where AI Meets Analyst Efficiency
Credit and risk functions in both financial- and non-financial institutions are constantly seeking ways to drive efficiencies in their credit risk management workflows. While artificial intelligence (AI) can be used to complement traditional credit risk analysis, leveraging innovative AI capabilities to automate processes such as credit memo generation holds perhaps the greatest promise in enabling credit risk analysts to make faster, smarter decisions. The credit memo’s strict creation guidelines, established structure, well-defined context, and a large amount of source material —such as industry reports, research, news, financial statements—make it a natural candidate for generative AI automation.
Credit leaders across the globe tell us they have three key goals in building better credit memos: reducing time spent collecting and summarizing information, refreshing data for analysis in a timely manner, and consistently introducing new structured and unstructured sources of information across their teams. There also are a set of common challenges that, in aggregate, thwarts their ability to utilize AI to achieve greater efficiencies:
- Limited access to high-quality, usable content: This includes structured, unstructured, and alternative data from multiple reliable sources that can be used (with appropriate IP rights) in the context of AI.
- Solutions in search of a problem: Like many tech innovations, these solutions often prioritize technology over understanding the task context. These technology-first solutions may offer powerful large language models, but they lack the context awareness needed to support allocation decisions or applications that align with a credit analyst's workflow.
- Clear ROI calculations: Because the technology is so new to many credit leaders, understanding the nuances of project costs - both for developing AI agents and preparing content for AI use, as well as operational costs - presents a risk that the investment might not deliver the promised return.
The ROI challenge: Preparing content for AI and developing a content expert agentic framework can take several months and millions of dollars to develop –assuming that the rights to use the content for training and consumption by the agentic framework have been acquired. It is not a one-time cost, as the technology is evolving rapidly and requires constant refinement and expansion.
Based on several proofs of concept in our innovation labs, we see the potential to save up to 50% of an analyst's time by utilizing AI technologies to produce a complex credit memo, along with additional benefits of speed-to-completion, greater information leverage, and the ability to “refresh with a click”.
For a successful implementation, we have identified six best practices:
- Set the right expectations for automation: Today, agentic frameworks are excellent at summarizing information and attributing it to, say, a financial trend, as long as a connection exists somewhere in the available body of content. While a Gen AI model will readily create original connections and explanations, it is not yet fully reliable.
Define the scope: Analyze the existing memo, section by section, to identify where the content originates and what level of automation can be involved, if any. - Analyst in the loop: While agentic frameworks can do the heavy lifting of sourcing the right information and placing it in the appropriate section of a memo, an analyst should always run a sanity check on the overall narrative and have the ability to prompt sections to expand, summarize, or integrate with other documents as needed.
- Be wary of AI overuse: Running AI models introduces operational risk and potential errors and also takes time for training and tuning. Much of the information (e.g., financial tables, projections) in a credit memo is “compiled” and may be best handled with Excel templates populated with APIs or add-ins, rather than by introducing unnecessary technological complexity or spending excessive time refining AI models or preventing hallucinations.
- Track to ensure efficiency gains: Disruptive technologies can enable better use of resources and drive efficiencies in ways previously not possible. Companies planning to use AI to generate credit memos should diligently track the time and cost savings achieved over traditional methods. The ability to quickly integrate more information and easily refresh memos could lead to more insightful and timely outputs, with a potential trade-off in transparency. Faster doesn’t necessarily mean better.
- Start small and roll-out incrementally: Begin by identifying a champion team to support an initial proof of concept and test/refine specific memo sections. Then implement a train-the-trainer model to roll out the memo to additional user groups.
- Choose a partner with risk-domain expertise: Many tech companies are building agentic frameworks to fetch data and contextualize it through human-like narratives. Work with a reliable partner who keeps pace with technological evolution and understands the nuances of risk-domain requirements.